Boosting Agnostic Fundamental Analysis: Using Machine Learning to Identify Mispricing in European Stock Markets

Matthias X. Hanauer, Marina Kononova, Marc Steffen Rapp*

*Corresponding author af dette arbejde

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Abstract

Interested in fundamental analysis and inspired by Bartram and Grinblatt (2018, 2021), we apply linear regression (LR) and tree-based machine learning (ML) methods to estimate monthly peer-implied fair values of European stocks from 21 accounting variables. Comparing LR and ML models, we document substantial heterogeneity in the importance of predictors as measured by SHAP values. Examining trading strategies based on deviations from fair values, we find ML-strategies earn substantially higher risk-adjusted returns (“alpha”) than simple LR-counterparts (48–66 vs. 11–36 bp per month for value-weighted portfolios). Our findings document the importance of allowing for non-linearities and interactions in fundamental analysis.
OriginalsprogEngelsk
Artikelnummer102856
TidsskriftFinance Research Letters
Vol/bind48
Antal sider10
ISSN1544-6123
DOI
StatusUdgivet - aug. 2022

Emneord

  • Fundamental analysis
  • Market efficiency
  • Stock return
  • Machine learning
  • Random forest
  • Gradient boosting
  • European Markets

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